Solar and Terrestrial Radiation

Bayesian inference has found widespread application and use in science and engineering to reconcile Earth system models with data, including prediction in space (interpolation), prediction in time (forecasting), assimilation of observations and deterministic/stochastic model output, and inference of the model parameters. Bayes theorem states that the posterior probability, p ( H | Y ˜ ) of a hypothesis, H is proportional to the product of the prior probability, p(H) of this hypothesis and the likelihood, L ( H | Y ˜ ) of the same hypothesis given the new observations, Y ˜ , or p ( H | Y ˜ ) ∝ p ( H ) L ( H | Y ˜ ) . In science and engineering, H often constitutes some numerical model, ℱ(x) which summarizes, in algebraic and differential equations, state variables and fluxes, all knowledge of the system of interest, and the unknown parameter values, x are subject to inference using the data Y ˜ . Unfortunately, for complex system models the posterior distribution is often high dimensional and analytically intractable, and sampling methods are required to approximate the target. In this paper I review the basic theory of Markov chain Monte Carlo (MCMC) simulation and introduce a MATLAB toolbox of the DiffeRential Evolution Adaptive Metropolis (DREAM) algorithm developed by Vrugt et al. (2008a, 2009a) and used for Bayesian inference in fields ranging from physics, chemistry and engineering, to ecology, hydrology, and geophysics. This MATLAB toolbox provides scientists and engineers with an arsenal of options and utilities to solve posterior sampling problems involving (among others) bimodality, high-dimensionality, summary statistics, bounded parameter spaces, dynamic simulation models, formal/informal likelihood functions (GLUE), diagnostic model evaluation, data assimilation, Bayesian model averaging, distributed computation, and informative/noninformative prior distributions. The DREAM toolbox supports parallel computing and includes tools for convergence analysis of the sampled chain trajectories and post-processing of the results. Seven different case studies illustrate the main capabilities and functionalities of the MATLAB toolbox.

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Markov chain Monte Carlo simulation using the DREAM software package: Theory, concepts, and MATLAB implementation

Aim
To describe the patterns and trends in the spread of crop pests and pathogens around the world, and determine the socioeconomic, environmental and biological factors underlying the rate and degree of redistribution of crop-destroying organisms.

Location
Global.

Methods
Current country- and state-level distributions of 1901 pests and pathogens and historical observation dates for 424 species were compared with potential distributions based upon distributions of host crops. The degree of ‘saturation’, i.e. the fraction of the potential distribution occupied, was related to pest type, host range, crop production, climate and socioeconomic variables using linear models.

Results
More than one-tenth of all pests have reached more than half the countries that grow their hosts. If current trends continue, many important crop-producing countries will be fully saturated with pests by the middle of the century. While dispersal increases with host range overall, fungi have the narrowest host range but are the most widely dispersed group. The global dispersal of some pests has been rapid, but pest assemblages remain strongly regionalized and follow the distributions of their hosts. Pest assemblages are significantly correlated with socioeconomics, climate and latitude. Tropical staple crops, with restricted latitudinal ranges, tend to be more saturated with pests and pathogens than temperate staples with broad latitudinal ranges. We list the pests likely to be the most invasive in coming years.

Main conclusions
Despite ongoing dispersal of crop pests and pathogens, the degree of biotic homogenization of the globe remains moderate and regionally constrained, but is growing. Fungal pathogens lead the global invasion of agriculture, despite their more restricted host range. Climate change is likely to influence future distributions. Improved surveillance would reveal greater levels of invasion, particularly in developing countries.

https://onlinelibrary.wiley.com/doi/pdf/10.1111/geb.12214

The global spread of crop pests and pathogens

How do weather and climate influence cropping area and intensity

Analyzing the heat island magnitude and characteristics in one hundred Asian and Australian cities and regions

Aim
Recent changes in crop yields have implications for future global food security, which are likely to be affected by climate change. We developed a spatially explicit global dataset of historical yields for maize, soybean, rice and wheat to explore the historical changes in mean, year-to-year variation and annual rate of change in yields for the period 1982–2006.

Location
This study was conducted at the global scale.

Methods
We modelled historical and spatial patterns of yields at a grid size of 1.125° by combining global agricultural datasets related to the crop calendar and harvested area in 2000, country yield statistics and satellite-derived net primary production. Modelled yields were compared with other global datasets of yields in 2000 (M3-Crops and MapSPAM) and subnational yield statistics for 23 major crop-producing countries. Historical changes in modelled yields were then examined.

Results
Modelled yields explained 45–81% of the spatial variation of yields in 2000 from M3-Crops and MapSPAM, with root-mean-square errors of 0.5–1.8 t ha−1. Most correlation coefficients between modelled yield time series and subnational yield statistics for the period 1982–2006 in major crop-producing regions were greater than 0.8. Our analysis corroborated the incidence of reported yield stagnations and collapses and showed that low and mid latitudes in the Southern Hemisphere (0–40°S) experienced significantly increased year-to-year variation in maize, rice and wheat yields in 1994–2006 compared with that in 1982–93.

Main conclusions
Our analyses revealed increased instability of yields across a broad region of the Southern Hemisphere, where many developing countries are located. Such changes are likely to be related to recent yield stagnation and collapses. Although our understanding of the impacts of recent climate change, particularly the incidence of climate extremes, on crop yields remains limited, our dataset offers opportunities to close parts of this knowledge gap.

https://onlinelibrary.wiley.com/doi/pdf/10.1111/geb.12120

Historical changes in global yields: Major cereal and legume crops from 1982 to 2006

Projected global warming is expected to increase the occurrence of heat-induced spikelet sterility (HISS) of rice (Oryza sativa L.). Previous chamber experiments have shown that HISS can occur where temperature at flowering time exceeds the threshold temperature of around 35°C. The occurrence of HISS is, however, difficult to predict because the thermal conditions of rice canopy can be different from the air temperature under field condition. To cope with this, we developed a simple micrometeorology model focusing canopy and panicle temperatures; IM2PACT (Integrated Micrometeorology Model for Panicle And Canopy Temperature) as a tool to be incorporated into general meteorology databases. The IM2PACT was validated by leaf transpiration retarding (RT) experiment. During their flowering stage, leaf temperature elevated by RT treatment, which caused warming and drying of the air inside the canopy, resulting in the elevation of the panicle temperature (Tp). The IM2PACT well simulated the RT experiment, and was proved to simulate not only the Tp magnitude but also the effects on Tp of leaf transpiration characteristics via changes in micrometeorology inside the canopy. The IM2PACT was applied to the meteorology dataset based on ANEMOS in order to analyse the Tp at Kanto and Tokai regions of extremely hot summer in 2007. There was a great gap in spatial distributions between the Tp and the daily maximum air temperature which is commonly used as a measure of HISS, because the difference of meteorology, especially relative humidity, among areas altered the panicle-air temperature difference. This strongly suggests that we must refer to the Tp instead of the air temperature in daily maximum, as a measure variable for HISS. The IM2PACT is a powerful tool to elucidate the Tp in the climate change impact study to bridge between the responses of crop susceptible to heat and the meteorological data.

Integrated micrometeorology model for panicle and canopy temperature (IM2PACT) for rice heat stress studies under climate change

Air-Lake Interaction Features Found in Heat and Water Exchanges over Nam Co on the Tibetan Plateau

Rain-fed lowlands are major agricultural ecosystems used for rice production in Northeast Thailand. Developing a tool to assess the effects of variable water conditions on the regional scale yield is also important to predict the effects of climate change on food supply. To estimate regional yields, we need a simple but accurate measure of the crop calendar (i.e., the distribution of planting dates), which has a strong influence on grain yield. In this article, we modeled the dependence of the crop calendar on rainfall patterns based on a survey of the region’s farmers as a part of an effort to provide a stronger basis for regional yield estimates. Our survey, conducted in 11 provinces for 2 years, confirmed the existence of large windows for sowing and transplanting versus narrow windows for heading and harvesting for rain-fed lowland rice culture in all the provinces. Variable water, soil, and toposequential conditions in the paddy fields were responsible for the large sowing and transplanting windows, whereas the use of photoperiod-sensitive varieties explained the narrow windows for heading and harvesting. The crop calendar was well expressed as a function of cumulative precipitation from June onward. When the crop calendar model was combined with a simple phenology-based model that uses growing degree-days adjusted by a day-length factor, we could estimate the rice crop calendar under rain-fed lowland conditions with acceptable accuracy. The model described in this article will be combined with a crop growth model to improve regional yield estimates for rain-fed lowland rice.

Modeling the dependence of the crop calendar for rain-fed rice on precipitation in Northeast Thailand

Climate change will have significant impacts on the rain-fed rice production ecosystem, and particularly on the ecosystem’s hydrology and water resources. Under rain-fed lowland conditions, substantial variations among fields in grain yield are commonly observed, but a method that can account for field-scale yield variability to produce regional-scale yield estimates is lacking, thereby limiting our ability to predict future rice production under changing climate and variable water resources. In this study, we developed a model for estimating regional yields of rain-fed lowland rice in Northeast Thailand, by combining a simple crop model with a crop calendar model. The crop model incorporates the effects of two important resources (water and nitrogen) on crop growth. The biomass accumulation is driven by water use, whereas the nitrogen supply determines canopy development and thereby constrains crop water use. Accounting for the wide range of planting dates and the strong photoperiod-sensitive characteristics of rice varieties through the calendar model is an essential component in determining regional yield estimates. The present model does not account for the effects of mid-season drought or flooding, but was nonetheless able to explain the spatial and temporal yield variations at the province level for the past 25 years. Thus, it can be used as a prototype for simulating regional yields of rain-fed lowland rice.

Yasushi Ishigooka

Papers

Historical changes in global yields: Major cereal and legume crops from 1982 to 2006

Integrated micrometeorology model for panicle and canopy temperature (IM2PACT) for rice heat stress studies under climate change

Air-Lake Interaction Features Found in Heat and Water Exchanges over Nam Co on the Tibetan Plateau

Modeling the dependence of the crop calendar for rain-fed rice on precipitation in Northeast Thailand

A model driven by crop water use and nitrogen supply for simulating changes in the regional yield of rain-fed lowland rice in Northeast Thailand